RiboMethSeq is an RNAseq-based approach to analyze 2’O-ribose methylation (2’Ome).
rRMSAnalyzer is an R package that provides a set of easy-to-use functions to evaluate 2’Ome levels by computing C-scores from RiboMethSeq read end counts as input.
Available features (version 3):
- C-score computation
- Batch effect adjustment with CombatSeq
- Different visualizations to compare samples or sites
- Include a table of annotated human rRNA sites
- Export computed C-scores into a dataframe
- Three novel functions to compute automatically reports (QC report and 2 analytic reports dedicated to 2’Ome profiles and site-by-site change in 2’Ome)
Note: We have also developed a dedicated Nextflow pipeline (ribomethseq-nf) to process the data from sequencing output (fastq files) to useful raw data for rRMSAnalyzer (read end counts).
Usage
library(rRMSAnalyzer)
ribo <- load_ribodata(
count_path = "/path/to/your/csvfiles/directory/",
metadata = "path/to/metadata.csv",
metadata_key = "filename",
metadata_id = "samplename")
# Compute the C-score using different parameters,
# including calculation of the local coverage using the mean instead of the median
ribo <- compute_cscore(ribo, method = "mean")
# If necessary, adjust any technical biases using ComBat-Seq.
# Here, as an example, we use the "library" column in metadata.
ribo <- adjust_bias(ribo,"library")
# Plot a Principal Component Analysis (PCA) whose colors depend on the "condition" column in metadata
plot_pca(ribo,"condition")Getting started
The “getting started” is available on our Github page: https://ribosomecrcl.github.io/rRMSAnalyzer/
A test dataset (ribo_toy) is included in the package.
Help, bug reports and suggestions
To report a bug or any suggestion to improve the package, please let us known by opening a new issue on: https://github.com/RibosomeCRCL/rRMSAnalyzer/issues